Overview

Dataset statistics

Number of variables18
Number of observations4600
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory647.0 KiB
Average record size in memory144.0 B

Variable types

NUM12
CAT5
BOOL1

Warnings

country has constant value "4600" Constant
date has a high cardinality: 70 distinct values High cardinality
street has a high cardinality: 4525 distinct values High cardinality
statezip has a high cardinality: 77 distinct values High cardinality
price is highly skewed (γ1 = 24.79093256) Skewed
street is uniformly distributed Uniform
price has 49 (1.1%) zeros Zeros
view has 4140 (90.0%) zeros Zeros
sqft_basement has 2745 (59.7%) zeros Zeros
yr_renovated has 2735 (59.5%) zeros Zeros

Reproduction

Analysis started2022-03-08 12:43:20.865074
Analysis finished2022-03-08 12:44:04.325945
Duration43.46 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

date
Categorical

HIGH CARDINALITY

Distinct70
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
2014-06-23 00:00:00
 
142
2014-06-25 00:00:00
 
131
2014-06-26 00:00:00
 
131
2014-07-08 00:00:00
 
127
2014-07-09 00:00:00
 
121
Other values (65)
3948 
ValueCountFrequency (%) 
2014-06-23 00:00:001423.1%
 
2014-06-25 00:00:001312.8%
 
2014-06-26 00:00:001312.8%
 
2014-07-08 00:00:001272.8%
 
2014-07-09 00:00:001212.6%
 
2014-06-24 00:00:001202.6%
 
2014-05-20 00:00:001162.5%
 
2014-07-01 00:00:001162.5%
 
2014-06-17 00:00:001132.5%
 
2014-05-28 00:00:001112.4%
 
Other values (60)337273.3%
 
2022-03-08T18:14:04.569705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-03-08T18:14:04.834651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

price
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct1741
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551962.9885
Minimum0
Maximum26590000
Zeros49
Zeros (%)1.1%
Memory size35.9 KiB
2022-03-08T18:14:05.109312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200000
Q1322875
median460943.4615
Q3654962.5
95-th percentile1184050
Maximum26590000
Range26590000
Interquartile range (IQR)332087.5

Descriptive statistics

Standard deviation563834.7025
Coefficient of variation (CV)1.021508171
Kurtosis1044.352151
Mean551962.9885
Median Absolute Deviation (MAD)157500
Skewness24.79093256
Sum2539029747
Variance3.179095718e+11
MonotocityNot monotonic
2022-03-08T18:14:05.434841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0491.1%
 
300000420.9%
 
400000310.7%
 
450000290.6%
 
440000290.6%
 
600000290.6%
 
350000280.6%
 
435000270.6%
 
250000270.6%
 
550000270.6%
 
Other values (1731)428293.1%
 
ValueCountFrequency (%) 
0491.1%
 
78001< 0.1%
 
800001< 0.1%
 
830001< 0.1%
 
833002< 0.1%
 
ValueCountFrequency (%) 
265900001< 0.1%
 
128990001< 0.1%
 
70625001< 0.1%
 
46680001< 0.1%
 
44890001< 0.1%
 

bedrooms
Real number (ℝ≥0)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.400869565
Minimum0
Maximum9
Zeros2
Zeros (%)< 0.1%
Memory size35.9 KiB
2022-03-08T18:14:05.685467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9088481155
Coefficient of variation (CV)0.2672399215
Kurtosis1.235377429
Mean3.400869565
Median Absolute Deviation (MAD)1
Skewness0.456446633
Sum15644
Variance0.8260048971
MonotocityNot monotonic
2022-03-08T18:14:05.872924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3203244.2%
 
4153133.3%
 
256612.3%
 
53537.7%
 
6611.3%
 
1380.8%
 
7140.3%
 
02< 0.1%
 
82< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
02< 0.1%
 
1380.8%
 
256612.3%
 
3203244.2%
 
4153133.3%
 
ValueCountFrequency (%) 
91< 0.1%
 
82< 0.1%
 
7140.3%
 
6611.3%
 
53537.7%
 

bathrooms
Real number (ℝ≥0)

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.160815217
Minimum0
Maximum8
Zeros2
Zeros (%)< 0.1%
Memory size35.9 KiB
2022-03-08T18:14:06.118131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7837810747
Coefficient of variation (CV)0.3627247107
Kurtosis1.86590471
Mean2.160815217
Median Absolute Deviation (MAD)0.5
Skewness0.6160327234
Sum9939.75
Variance0.614312773
MonotocityNot monotonic
2022-03-08T18:14:06.365112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
2.5118925.8%
 
174316.2%
 
1.7562913.7%
 
24279.3%
 
2.254199.1%
 
1.52916.3%
 
2.752766.0%
 
31673.6%
 
3.51623.5%
 
3.251363.0%
 
Other values (16)1613.5%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.75170.4%
 
174316.2%
 
1.2530.1%
 
1.52916.3%
 
ValueCountFrequency (%) 
81< 0.1%
 
6.751< 0.1%
 
6.51< 0.1%
 
6.252< 0.1%
 
5.751< 0.1%
 

sqft_living
Real number (ℝ≥0)

Distinct566
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2139.346957
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Memory size35.9 KiB
2022-03-08T18:14:06.631110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile950
Q11460
median1980
Q32620
95-th percentile3870
Maximum13540
Range13170
Interquartile range (IQR)1160

Descriptive statistics

Standard deviation963.2069158
Coefficient of variation (CV)0.4502340833
Kurtosis8.2916826
Mean2139.346957
Median Absolute Deviation (MAD)570
Skewness1.723513271
Sum9840996
Variance927767.5626
MonotocityNot monotonic
2022-03-08T18:14:06.927909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1940320.7%
 
1720320.7%
 
1660310.7%
 
1840310.7%
 
2000300.7%
 
1410290.6%
 
1200280.6%
 
1480280.6%
 
1490270.6%
 
1890270.6%
 
Other values (556)430593.6%
 
ValueCountFrequency (%) 
3701< 0.1%
 
3801< 0.1%
 
4201< 0.1%
 
4301< 0.1%
 
4901< 0.1%
 
ValueCountFrequency (%) 
135401< 0.1%
 
100401< 0.1%
 
96401< 0.1%
 
86701< 0.1%
 
80201< 0.1%
 

sqft_lot
Real number (ℝ≥0)

Distinct3113
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14852.51609
Minimum638
Maximum1074218
Zeros0
Zeros (%)0.0%
Memory size35.9 KiB
2022-03-08T18:14:07.069716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum638
5-th percentile1690.8
Q15000.75
median7683
Q311001.25
95-th percentile43560
Maximum1074218
Range1073580
Interquartile range (IQR)6000.5

Descriptive statistics

Standard deviation35884.43614
Coefficient of variation (CV)2.416050987
Kurtosis219.8729874
Mean14852.51609
Median Absolute Deviation (MAD)2772
Skewness11.30713875
Sum68321574
Variance1287692757
MonotocityNot monotonic
2022-03-08T18:14:07.219077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5000801.7%
 
6000651.4%
 
4000541.2%
 
7200501.1%
 
4800290.6%
 
9600250.5%
 
4500250.5%
 
5500230.5%
 
3000230.5%
 
7500230.5%
 
Other values (3103)420391.4%
 
ValueCountFrequency (%) 
6381< 0.1%
 
6811< 0.1%
 
7041< 0.1%
 
7461< 0.1%
 
7471< 0.1%
 
ValueCountFrequency (%) 
10742181< 0.1%
 
6412031< 0.1%
 
4782881< 0.1%
 
4356002< 0.1%
 
4238381< 0.1%
 

floors
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.512065217
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Memory size35.9 KiB
2022-03-08T18:14:07.329846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5382883773
Coefficient of variation (CV)0.3559954763
Kurtosis-0.5388519795
Mean1.512065217
Median Absolute Deviation (MAD)0.5
Skewness0.5514406463
Sum6955.5
Variance0.2897543771
MonotocityNot monotonic
2022-03-08T18:14:07.425904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
1217447.3%
 
2181139.4%
 
1.54449.7%
 
31282.8%
 
2.5410.9%
 
3.52< 0.1%
 
ValueCountFrequency (%) 
1217447.3%
 
1.54449.7%
 
2181139.4%
 
2.5410.9%
 
31282.8%
 
ValueCountFrequency (%) 
3.52< 0.1%
 
31282.8%
 
2.5410.9%
 
2181139.4%
 
1.54449.7%
 

waterfront
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
0
4567 
1
 
33
ValueCountFrequency (%) 
0456799.3%
 
1330.7%
 
2022-03-08T18:14:07.504045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

view
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2406521739
Minimum0
Maximum4
Zeros4140
Zeros (%)90.0%
Memory size35.9 KiB
2022-03-08T18:14:07.566531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7784047172
Coefficient of variation (CV)3.234563414
Kurtosis10.46417792
Mean0.2406521739
Median Absolute Deviation (MAD)0
Skewness3.341586381
Sum1107
Variance0.6059139038
MonotocityNot monotonic
2022-03-08T18:14:07.644635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
0414090.0%
 
22054.5%
 
31162.5%
 
4701.5%
 
1691.5%
 
ValueCountFrequency (%) 
0414090.0%
 
1691.5%
 
22054.5%
 
31162.5%
 
4701.5%
 
ValueCountFrequency (%) 
4701.5%
 
31162.5%
 
22054.5%
 
1691.5%
 
0414090.0%
 

condition
Real number (ℝ≥0)

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.45173913
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size35.9 KiB
2022-03-08T18:14:07.753985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6772297676
Coefficient of variation (CV)0.19619958
Kurtosis0.1977302051
Mean3.45173913
Median Absolute Deviation (MAD)0
Skewness0.9590676635
Sum15878
Variance0.4586401581
MonotocityNot monotonic
2022-03-08T18:14:07.847712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
3287562.5%
 
4125227.2%
 
54359.5%
 
2320.7%
 
160.1%
 
ValueCountFrequency (%) 
160.1%
 
2320.7%
 
3287562.5%
 
4125227.2%
 
54359.5%
 
ValueCountFrequency (%) 
54359.5%
 
4125227.2%
 
3287562.5%
 
2320.7%
 
160.1%
 

sqft_above
Real number (ℝ≥0)

Distinct511
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1827.265435
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Memory size35.9 KiB
2022-03-08T18:14:07.959205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile860
Q11190
median1590
Q32300
95-th percentile3440
Maximum9410
Range9040
Interquartile range (IQR)1110

Descriptive statistics

Standard deviation862.168977
Coefficient of variation (CV)0.4718356515
Kurtosis4.070138265
Mean1827.265435
Median Absolute Deviation (MAD)490
Skewness1.494210748
Sum8405421
Variance743335.3448
MonotocityNot monotonic
2022-03-08T18:14:08.099793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1010471.0%
 
1200471.0%
 
1300451.0%
 
1140441.0%
 
1320430.9%
 
1150420.9%
 
1090400.9%
 
1180400.9%
 
1400380.8%
 
1050370.8%
 
Other values (501)417790.8%
 
ValueCountFrequency (%) 
3701< 0.1%
 
3801< 0.1%
 
4201< 0.1%
 
4301< 0.1%
 
4901< 0.1%
 
ValueCountFrequency (%) 
94101< 0.1%
 
80201< 0.1%
 
76801< 0.1%
 
73201< 0.1%
 
66401< 0.1%
 

sqft_basement
Real number (ℝ≥0)

ZEROS

Distinct207
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.0815217
Minimum0
Maximum4820
Zeros2745
Zeros (%)59.7%
Memory size35.9 KiB
2022-03-08T18:14:08.221973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3610
95-th percentile1210
Maximum4820
Range4820
Interquartile range (IQR)610

Descriptive statistics

Standard deviation464.1372281
Coefficient of variation (CV)1.487230726
Kurtosis4.082380024
Mean312.0815217
Median Absolute Deviation (MAD)0
Skewness1.642732192
Sum1435575
Variance215423.3665
MonotocityNot monotonic
2022-03-08T18:14:08.475503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0274559.7%
 
500531.2%
 
600451.0%
 
800430.9%
 
900410.9%
 
700380.8%
 
1000330.7%
 
400330.7%
 
550270.6%
 
750260.6%
 
Other values (197)151633.0%
 
ValueCountFrequency (%) 
0274559.7%
 
201< 0.1%
 
501< 0.1%
 
602< 0.1%
 
651< 0.1%
 
ValueCountFrequency (%) 
48201< 0.1%
 
41301< 0.1%
 
28501< 0.1%
 
27301< 0.1%
 
25502< 0.1%
 

yr_built
Real number (ℝ≥0)

Distinct115
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.786304
Minimum1900
Maximum2014
Zeros0
Zeros (%)0.0%
Memory size35.9 KiB
2022-03-08T18:14:08.756921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1913
Q11951
median1976
Q31997
95-th percentile2009
Maximum2014
Range114
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.73184839
Coefficient of variation (CV)0.0150862873
Kurtosis-0.6700759004
Mean1970.786304
Median Absolute Deviation (MAD)23
Skewness-0.50215519
Sum9065617
Variance883.9828087
MonotocityNot monotonic
2022-03-08T18:14:09.038100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20061112.4%
 
20051042.3%
 
2007932.0%
 
2004922.0%
 
1978902.0%
 
2003891.9%
 
2008891.9%
 
1967821.8%
 
1977801.7%
 
2014781.7%
 
Other values (105)369280.3%
 
ValueCountFrequency (%) 
1900220.5%
 
190190.2%
 
1902100.2%
 
1903100.2%
 
190490.2%
 
ValueCountFrequency (%) 
2014781.7%
 
2013571.2%
 
2012330.7%
 
2011240.5%
 
2010280.6%
 

yr_renovated
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808.6082609
Minimum0
Maximum2014
Zeros2735
Zeros (%)59.5%
Memory size35.9 KiB
2022-03-08T18:14:09.336901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31999
95-th percentile2011
Maximum2014
Range2014
Interquartile range (IQR)1999

Descriptive statistics

Standard deviation979.4145364
Coefficient of variation (CV)1.211234888
Kurtosis-1.851110913
Mean808.6082609
Median Absolute Deviation (MAD)0
Skewness0.3859187009
Sum3719598
Variance959252.8341
MonotocityNot monotonic
2022-03-08T18:14:09.632400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0273559.5%
 
20001703.7%
 
20031513.3%
 
20091092.4%
 
20011092.4%
 
2005952.1%
 
2004771.7%
 
2014721.6%
 
2006681.5%
 
2013611.3%
 
Other values (50)95320.7%
 
ValueCountFrequency (%) 
0273559.5%
 
1912330.7%
 
19131< 0.1%
 
1923571.2%
 
193460.1%
 
ValueCountFrequency (%) 
2014721.6%
 
2013611.3%
 
2012451.0%
 
2011541.2%
 
2010300.7%
 

street
Categorical

HIGH CARDINALITY
UNIFORM

Distinct4525
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
2520 Mulberry Walk NE
 
4
2500 Mulberry Walk NE
 
3
2300 14th Ave S
 
2
611 N 46th St
 
2
21132 NE 42nd St
 
2
Other values (4520)
4587 
ValueCountFrequency (%) 
2520 Mulberry Walk NE40.1%
 
2500 Mulberry Walk NE30.1%
 
2300 14th Ave S2< 0.1%
 
611 N 46th St2< 0.1%
 
21132 NE 42nd St2< 0.1%
 
2008 Yale Ave E2< 0.1%
 
513 N 46th St2< 0.1%
 
9126 45th Ave SW2< 0.1%
 
11034 NE 26th Pl2< 0.1%
 
323 25th Ave S2< 0.1%
 
Other values (4515)457799.5%
 
2022-03-08T18:14:09.978351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4453 ?
Unique (%)96.8%
2022-03-08T18:14:10.287928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length46
Median length16
Mean length17.01826087
Min length8

city
Categorical

Distinct44
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
Seattle
1573 
Renton
293 
Bellevue
286 
Redmond
235 
Issaquah
 
187
Other values (39)
2026 
ValueCountFrequency (%) 
Seattle157334.2%
 
Renton2936.4%
 
Bellevue2866.2%
 
Redmond2355.1%
 
Issaquah1874.1%
 
Kirkland1874.1%
 
Kent1854.0%
 
Auburn1763.8%
 
Sammamish1753.8%
 
Federal Way1483.2%
 
Other values (34)115525.1%
 
2022-03-08T18:14:10.601712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.1%
2022-03-08T18:14:10.884283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length7
Mean length7.753913043
Min length4

statezip
Categorical

HIGH CARDINALITY

Distinct77
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
WA 98103
 
148
WA 98052
 
135
WA 98117
 
132
WA 98115
 
130
WA 98006
 
110
Other values (72)
3945 
ValueCountFrequency (%) 
WA 981031483.2%
 
WA 980521352.9%
 
WA 981171322.9%
 
WA 981151302.8%
 
WA 980061102.4%
 
WA 980591062.3%
 
WA 980421002.2%
 
WA 98034992.2%
 
WA 98074982.1%
 
WA 98053982.1%
 
Other values (67)344474.9%
 
2022-03-08T18:14:11.206041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-03-08T18:14:11.720738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

country
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
USA
4600 
ValueCountFrequency (%) 
USA4600100.0%
 
2022-03-08T18:14:11.830088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-08T18:14:11.908191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:11.955057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2022-03-08T18:13:38.086601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:38.278326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:38.395558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:38.500598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:38.611064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:38.720413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:38.829762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:38.939117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:39.065335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:39.159137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:39.268486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:39.377843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:39.502778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:39.711210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:39.930190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:40.153369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:40.372263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:40.590962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:40.812200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:41.048041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:41.289715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:41.489147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:41.707846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:41.926550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:42.303700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:42.538680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:42.701720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:42.826691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:42.936034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.061006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.170359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.295330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.420301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.529660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.639311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.748658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.873627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:43.984616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:44.219182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:44.484746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:44.734687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:44.984866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:45.221517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:45.487745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:45.686574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:45.795924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:45.905272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.014622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.155324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.264666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.374017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.483364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.592715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.702064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:46.920765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.045700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.164178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.264517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.373867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.483253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.602690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.712039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.821388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:47.930738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:48.040092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:48.159771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:48.263590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:48.375716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:48.485825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:48.583424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:48.763575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:49.004877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:49.248686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:49.514589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:49.776186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:50.055844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:50.352053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:50.620496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:50.878652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:51.154975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:51.421796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:51.656279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:51.916474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:52.168095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:52.441414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:52.691354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:52.925670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:53.196676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:53.368498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:53.483423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:53.608392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:53.873955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:53.998926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:54.108275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:54.233246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:54.342591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:54.483188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:54.576923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:54.717510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:54.967452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:55.186607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:55.405306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:55.624007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:55.858325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:56.092645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:56.296175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:56.530492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:56.749192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:56.983683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:57.215558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:57.434255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:57.668575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:57.840406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:57.949759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.043455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.152839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.277806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.371534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.480886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.590371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.699714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.809034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:58.902795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:59.023725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:59.125433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:59.237751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:59.425347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:59.659667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:13:59.913152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:00.151520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:00.385629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:00.604328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:00.854404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:01.132770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:01.389075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:01.649226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:01.899164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:02.150602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:02.572376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:02.698788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:02.823757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:02.933102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:03.058072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:03.174124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-03-08T18:14:12.064404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-08T18:14:12.283104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-08T18:14:12.470528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-08T18:14:12.673636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-03-08T18:14:12.845470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-03-08T18:14:03.498422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-08T18:14:03.842093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
02014-05-02 00:00:00313000.03.01.50134079121.5003134001955200518810 Densmore Ave NShorelineWA 98133USA
12014-05-02 00:00:002384000.05.02.50365090502.0045337028019210709 W Blaine StSeattleWA 98119USA
22014-05-02 00:00:00342000.03.02.001930119471.0004193001966026206-26214 143rd Ave SEKentWA 98042USA
32014-05-02 00:00:00420000.03.02.25200080301.00041000100019630857 170th Pl NEBellevueWA 98008USA
42014-05-02 00:00:00550000.04.02.501940105001.00041140800197619929105 170th Ave NERedmondWA 98052USA
52014-05-02 00:00:00490000.02.01.0088063801.0003880019381994522 NE 88th StSeattleWA 98115USA
62014-05-02 00:00:00335000.02.02.00135025601.000313500197602616 174th Ave NERedmondWA 98052USA
72014-05-02 00:00:00482000.04.02.502710358682.0003271001989023762 SE 253rd PlMaple ValleyWA 98038USA
82014-05-02 00:00:00452500.03.02.502430884261.000415708601985046611-46625 SE 129th StNorth BendWA 98045USA
92014-05-02 00:00:00640000.04.02.00152062001.500315200194520106811 55th Ave NESeattleWA 98115USA

Last rows

datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
45902014-07-08 00:00:00380680.5555564.02.50262083312.0003262001991013602 SE 186th PlRentonWA 98058USA
45912014-07-08 00:00:00396166.6666673.01.75188057521.0004940940194503529 SW Webster StSeattleWA 98126USA
45922014-07-08 00:00:00252980.0000004.02.50253081692.0003253001993037654 18th Pl SFederal WayWA 98003USA
45932014-07-08 00:00:00289373.3076923.02.50253846002.000325380201319235703 Charlotte Ave SEAuburnWA 98092USA
45942014-07-09 00:00:00210614.2857143.02.50161072232.0003161001994026306 127th Ave SEKentWA 98030USA
45952014-07-09 00:00:00308166.6666673.01.75151063601.00041510019541979501 N 143rd StSeattleWA 98133USA
45962014-07-09 00:00:00534333.3333333.02.50146075732.0003146001983200914855 SE 10th PlBellevueWA 98007USA
45972014-07-09 00:00:00416904.1666673.02.50301070142.00033010020090759 Ilwaco Pl NERentonWA 98059USA
45982014-07-10 00:00:00203400.0000004.02.00209066301.000310701020197405148 S Creston StSeattleWA 98178USA
45992014-07-10 00:00:00220600.0000003.02.50149081022.0004149001990018717 SE 258th StCovingtonWA 98042USA